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xinren08: 金币+20, ★★★★★最佳答案, 非常谢感谢了。 2016-03-28 15:34:05
lazy锦溪: LS-EPI+1, 感谢应助！ 2016-03-28 16:53:20
ccession number:       
20160601888937
        Title:        A new generalized alternating direction method of multipliers for separable convex programming and its applications to image deblurring with wavelets
        Authors:         Sun, Hongchun1 Email author sunhc68@163.com; Sun, Min2 Email author ziyouxiaodou@163.com; Zhou, Houchun1 Email author zhouhouchun@163.com
        Author affiliation:        1 School of Science Linyi University, Shuangling Street, Linyi, China
                2 School of Mathematics and Statistics Zaozhuang University, Beian Road, Zaozhuang, China
        Source title:        ICIC Express Letters
        Abbreviated source title:        ICIC Express Lett.
        Volume:        10
        Issue:        2
        Issue date:        February 1, 2016
        Publication year:        2016
        Pages:        271-277
        Language:        English
        ISSN:         1881803X
        Document type:        Journal article (JA)
        Publisher:        ICIC Express Letters Office
        Abstract:        The classical alternating direction method of multipliers (ADMM), which is a special case of the famous proximal point algorithm, is well studied in the literature. In this paper, we propose a new generalized ADMM for separable convex programming, which is a modification of the classical ADMM by introducing an acceleration factor, and includes the latter as a special case. We prove the global convergence of the proposed method under some standard assumptions. Numerical experiments about image deblurring with wavelets are included to illustrate the efficiency of the new method. © 2016, ICIC Express Letters ICIC International.
        Number of references:        7
        Main heading:         Image enhancement
        Controlled terms:         Algorithms - Convex optimization - Numerical methods - Variational techniques
        Uncontrolled terms:         Acceleration factors - Alternating direction method of multipliers - Global conver-gence - Image deblurring - Numerical experiments - Proximal point algorithm - Standard assumptions - Variational inequalities
        Classification code:         921.2 Calculus - 921.6 Numerical Methods
        Database:        Compendex
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